Bibliographic record
Abstract
An image fusion process should preserve all useful patterns from the source images while minimizing artifacts that could interfere with subsequent analyses or distract human observers. Given that it is nearly impossible to fuse images without introducing some form of distortion, measurements are necessary to present a fused image quality (IQ) for user analysis. 9.1 Introduction Image-quality measurement is as important as image fusion methods to guide developments for engineers, support learning methods for machines, and enhance trust with users. This chapter focuses on objective evaluation using quantitative metrics, whereas subjective evaluation will be discussed in Chapter 10. In order to objectively assess the performance of an image fusion method, a number of evaluation metrics, either objective or subjective, have been proposed. Studies on image fusion lack information that explicitly defines the applicability and feasibility of a specific fusion algorithm for a given application. Usually, a subjective evaluation is carried out to validate an objective assessment. However, identifying a reliable subjective score needs extensive experiments, which is expensive and cannot cover all possible conditions of interest. Typically, a robust performance model is required to account for the critical image fusion parameters and better assess the trend of image fusion performance quality.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".